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Suspension InsulatorThe suspension type insulator is the one which consists of a number of porcelain discs connected in series by metal links in the form of a string as shown in Figure-1.Potential Distribution over Suspension Insulator StringAs we know the string of suspension insulators consists of a number of porcelain discs connected in series through metal links. The porcelain portion of each disc is in between two metal links. Thus, the disc acts like a capacitor represented by C as shown in Figure-2.The capacitance C is called the mutual capacitance. If the string has only mutual capacitance, then the charging ... Read More
To determine if the type in the first argument is a subclass of second, use the numpy.issubsctype() method in Python numpy. The 1st and the 2nd argument are datatypes.StepsAt first, import the required library −import numpy as npUsing the issubsctype() method in Numpy. Checking whether the first argument is a subclass of the second argument −print("Result...", np.issubsctype(np.float16, np.float32)) print("Result...", np.issubsctype(np.int32, np.signedinteger)) print("Result...", np.issubsctype('i4', np.signedinteger)) print("Result...", np.issubsctype('S8', str)) print("Result...", np.issubsctype(np.array([45, 89]), int)) print("Result...", np.issubsctype(np.array([5., 25., 40.]), float))Exampleimport numpy as np # To determine if the type in the first argument is a subclass of second, use the numpy.issubsctype() method in ... Read More
To check whether float data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. The parameters are the dtype or object coercible to one.StepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Numpy. Checking for float datatype with different sizes −print("Result...", np.issubdtype(np.float16, np.float32)) print("Result...", np.issubdtype(np.float32, np.float16)) print("Result...", np.issubdtype(np.float64, np.float32)) print("Result...", np.issubdtype(np.float32, np.float64)) print("Result...", np.issubdtype(np.float16, np.float64)) print("Result...", np.issubdtype(np.float64, np.float16))Exampleimport numpy as np # To check whether float data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. # The ... Read More
# To check whether int data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy.# The parameters are the dtype or object coercible to oneStepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Numpy. Checking for int datatype with different sizes −print("Result...", np.issubdtype(np.int16, np.int32)) print("Result...", np.issubdtype(np.int32, np.int16)) print("Result...", np.issubdtype(np.int64, np.int32)) print("Result...", np.issubdtype(np.int32, np.int64)) print("Result...", np.issubdtype(np.int16, np.int64)) print("Result...", np.issubdtype(np.int64, np.int16))Exampleimport numpy as np # To check whether int data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. # ... Read More
To check whether similar data types of different sizes are not subdtypes of each other, use the numpy.issubdtype() method in Python Numpy. The parameters are the dtype or object coercible to one.StepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Nump to check for similar datatypes with different sizes. Checking for float datatype with different sizes −print("Result...", np.issubdtype(np.float32, np.float64)) print("Result...", np.issubdtype(np.float64, np.float32))Checking for int datatype with different sizes −print("Result...", np.issubdtype(np.int16, np.int32)) print("Result...", np.issubdtype(np.int32, np.int16)) print("Result...", np.issubdtype(np.int64, np.int32)) print("Result...", np.issubdtype(np.int32, np.int64))Exampleimport numpy as np # To check whether similar data types of different sizes are ... Read More
Three-Phase Transmission SystemA three-phase transmission system is the one in which three line conductors are used to transmit the AC electric power from generating station to the substations. The three-phase system is universally adopted for transmission of electric power.Depending upon the number of conductors used, the three-phase AC transmission system is classified into two types viz. −Three-Phase Three-Wire SystemThree-Phase Four-Wire SystemConductor Material Required in 3-Phase 3-Wire AC SystemConsider a three-phase three-wire AC system as shown in Figure-1, it has three line conductors and one earthed neutral wire. The three-phase three-wire system may be star connected (as shown in Figure-1) or ... Read More
The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. The 1st parameter, f is an Ndimensional array containing samples of a scalar function. The 2nd parameter is the varargs i.e. the spacing between f values. Default unitary spacing for all dimensions.The 3rd parameter is the edge_order{1, 2} i.e. the Gradient is calculated using N-th order accurate differences at the boundaries. Default − 1. The 4th parameter is the ... Read More
To determine whether the given object represents a scalar data-type, use the numpy.issctype() method. The method returns Boolean result of check whether rep is a scalar dtype. The first parameter is the rep. If rep is an instance of a scalar dtype, True is returned.If not, False is returned.StepsAt first, import the required library −import numpy as npUsing the issctype() method in Numpy −print("Result...", np.issctype(np.int32)) print("Result...", np.issctype(np.int64)) print("Result...", np.issctype(np.dtype('str'))) print("Result...", np.issctype(100)) print("Result...", np.issctype(25.9)) print("Result...", np.issctype(np.float32(22.3)))Exampleimport numpy as np # To determine whether the given object represents a scalar datatype, use the numpy.issctype() method # The method returns Boolean result ... Read More
To return the cumulative sum of array elements over a given axis treating NaNs as zero, use the nancumprod() method. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. The method returns a new array holding the result unless out is specified, in which it is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.The 1st parameter is the input array. The 2nd parameter is ... Read More
To return the cumulative sum of array elements over a given axis treating NaNs as zero, use the nancumprod() method. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. Cumulative works like, 5, 5+10, 5+10+15, 5+10+15+20.The 1st parameter is the input array. The 2nd parameter is the axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. The 3rd parameter is the type of the returned array and of the accumulator in ... Read More